A Mixed Method Survey of Characteristics of HIV Care Facilities: Medical Monitoring Project Facility Survey Project
Bibliographic record
Abstract
BACKGROUND: Measuring the capacity of HIV medical facilities to deliver quality treatment and prevention care to people with HIV is essential to the over 1 million Americans living with HIV and supports federal efforts to end the HIV epidemic. To fill this gap and complement the ongoing Medical Monitoring Project-which conducts annual surveys of people with HIV and periodic surveys of HIV care providers-the US Centers for Disease Control and Prevention (CDC) and RTI (Research Triangle Institute) International conducted the Medical Monitoring Project Facility Survey (MMPFS). OBJECTIVE: We aimed to describe the survey methods designed to achieve a high response rate from the 1022 facilities providing care to people with HIV as part of the Medical Monitoring Project-including frame development, survey instrument development, facility recruitment, and postsurvey data processing. METHODS: For the MMPFS, the CDC and RTI developed a sequential multimode data collection approach (paper, web, and phone), including an abbreviated nonresponse follow-up (NRFU) instrument and the collection of administrative data for all facilities. Data were then processed to produce raw, imputed, and weighted datasets. Analyses included comparisons of responses to the full survey and NRFU survey. RESULTS: The full MMPFS survey yielded 455 complete survey respondents and the NRFU survey yielded 59 complete survey responses, a combined response rate of 50.3% (514/1022). A nonresponse bias analysis comparing the 2 surveys found a significant difference in the raw datasets for 4 (12%) of the 34 categorical variables that were identical between the 2 surveys (all P>.0014). Weighted and imputed datasets were then generated and compared. There was no significant difference between the 2 datasets for any variable (all P>.05). CONCLUSIONS: The CDC and RTI's MMPFS methodology proved to be a valuable means of collecting data from HIV care providers and providing estimates for facility characteristics related to the provision of health care for people with HIV. The combined response rate allowed the CDC and RTI to generate facility-level estimates and an imputed dataset that can be linked to MMPFS patient data. The methods may be applied to other facility survey studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".